class_docstr: AMPnetPipe is the asynchronous version of the MultiProcessPipe implementation
  which avoids the bubble issue, by using stale weights and gradients.
  The implementation closely follows the paper: https://arxiv.org/abs/1705.09786
code: def __init__(self, **kwargs: Any) -> None:
      super().__init__(**kwargs)
  
file_path: fairscale/fairscale/experimental/nn/ampnet_pipe/pipe.py
imports: from typing import Any
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from fairscale.nn.pipe import AsyncPipe
  from .ampnet import AsyncAMPnetEventLoop
label_desc: Use image node_id 1 to create a new AMPnetPipe object from inherited base classes: AsyncPipe with example: obj = AMPnetPipe()